In this paper, we explore prior research and introduce a new methodology for classifying mental state levels based on EEG signals utilizing machine learning (ML). Our method proposes an optimized training method by introducing a validation set and a refined standardization process to rectify data leakage shortcomings observed in preceding studies. Furthermore, we establish novel benchmark figures for various models, including random forest and deep neural networks.